Fine-Tuning Mistral-7B with LoRA (Low Rank Adaptation)
GPT-4 Summary: Dive deep into the innovative world of fine-tuning language models with our comprehensive event, focusing on the groundbreaking Low-Rank Adaptation (LoRA) approach from Hugging Face's Parameter Efficient Fine-Tuning (PEFT) library. Discover how LoRA revolutionizes the industry by significantly reducing trainable parameters without sacrificing performance. Gain practical insights with a hands-on Python tutorial to adapt pre-trained LLMs for specific tasks. Whether you're a seasoned professional or just starting, this event will equip you with a deep understanding of efficient LLM fine-tuning. Join us live for an enlightening session on mastering PEFT and LoRA to transform your models!
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Speakers:
Dr. Greg, Co-Founder & CEO AI Makerspace
/ gregloughane
The Wiz, Co-Founder & CTO AI Makerspace
/ csalexiuk
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00:00:00 Efficient Fine-Tuning with Low Rank Adaptation
00:03:47 Optimizing AI Models for Business Efficiency
00:07:34 Unsupervised Pre-training and Instruction Tuning
00:11:16 Challenges of Large Language Models (LLMs)
00:15:00 Introduction to Parameter Efficient Fine-Tuning: LoRA Explained
00:19:00 Understanding Rank Decomposition in Transformer Models
00:22:42 Understanding Fine-tuning with PFT and Laura
00:26:51 Using Hugging Face Models and Alpaka GPT-4 Dataset
00:30:36 Optimizing Q and VR Modules with Low Rank Adaptation
00:34:27 Parameter Efficiency in Training Models
00:38:21 Synthetic Data Generation for Instruction Sets
00:41:49 Advancements in Model Inference and Production Flexibility
00:45:34 LLaMA Fine-Tuning with Attention Layers
00:49:34 Understanding Performance Trade-offs with LoRA Models
00:53:29 Getting Started with Collab for Model Training
00:57:09 Fine-Tuning Models for Portfolio Management